{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "########From MLia35  chp02 kNN \n",
    "#P17\n",
    "from numpy import *\n",
    "import operator\n",
    "import os \n",
    "import numpy as np\n",
    "os.chdir(\"/home/lab466/pythons/pyMLIA35/Ch02kNN\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "##P18   #k-近邻算法\n",
    "#import kNN\n",
    "#reload(kNN)\n",
    "#计算距离\n",
    "def classify0(inX,dataSet,labels,k):\n",
    "    dataSetSize=dataSet.shape[0]   #shape读取数据矩阵第一维度的长度\n",
    "    diffMat = tile(inX, (dataSetSize, 1)) - dataSet  #tile重复数组inX，有dataSet行 1个dataSet列，减法计算差值\n",
    "    sqDiffMat=diffMat**2 #**是幂运算的意思，这里用的欧式距离\n",
    "    sqDisttances=sqDiffMat.sum(axis=1) #普通sum默认参数为axis=0为普通相加，axis=1为一行的行向量相加\n",
    "    distances=sqDisttances**0.5\n",
    "    sortedDistIndicies=distances.argsort() #argsort返回数值从小到大的索引值（数组索引0,1,2,3）\n",
    " #选择距离最小的k个点\n",
    "    classCount={}\n",
    "    for i in range(k):\n",
    "        voteIlabel=labels[sortedDistIndicies[i]] #根据排序结果的索引值返回靠近的前k个标签\n",
    "        classCount[voteIlabel]=classCount.get(voteIlabel,0)+1 #各个标签出现频率\n",
    "    sortedClassCount = sorted(classCount.items(), key=operator.itemgetter(1), reverse=True) #排序频率\n",
    "    #!!!!!  classCount.iteritems()修改为classCount.items()\n",
    "    #sorted(iterable, cmp=None, key=None, reverse=False) --> new sorted list。\n",
    "    # reverse默认升序 key关键字排序itemgetter（1）按照第一维度排序(0,1,2,3)\n",
    "    return sortedClassCount[0][0]  #找出频率最高的"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#创建数据集\n",
    "def createDataSet():\n",
    "    group=array([[1.0,1.1],[1.0,1.0],[0,0],[0,0.1]])\n",
    "    labels=['A','A','B','B']\n",
    "    return group,labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def file2matrix(filename):\n",
    "    fr=open(filename)\n",
    "    arrayOLines=fr.readlines()\n",
    "    numberOfLines=len(arrayOLines) #读出数据行数\n",
    "    returnMat=zeros((numberOfLines,3))  #创建返回矩阵\n",
    "    classLabelVector=[]\n",
    "    index=0\n",
    "    for line in arrayOLines:\n",
    "        line=line.strip()  #删除空白符\n",
    "        listFromLine=line.split('\\t') #split指定分隔符对数据切片\n",
    "        returnMat[index,:]=listFromLine[0:3] #选取前3个元素（特征）存储在返回矩阵中\n",
    "        classLabelVector.append(int(listFromLine[-1]))\n",
    "        #-1索引表示最后一列元素,位label信息存储在classLabelVector\n",
    "        index+=1\n",
    "    return returnMat,classLabelVector\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#归一化特征值\n",
    "#归一化公式  ：（当前值-最小值）/range\n",
    "def autoNorm(dataSet):\n",
    "    minVals=dataSet.min(0) #存放每列最小值，参数0使得可以从列中选取最小值，而不是当前行\n",
    "    maxVals=dataSet.max(0) #存放每列最大值\n",
    "    ranges = maxVals - minVals\n",
    "    normDataSet=zeros(shape(dataSet))  #初始化归一化矩阵为读取的dataSet\n",
    "    m=dataSet.shape[0]  #m保存第一行\n",
    "    # 特征矩阵是3x1000，min max range是1x3 因此采用tile将变量内容复制成输入矩阵同大小\n",
    "    normDataSet=dataSet-tile(minVals,(m,1))\n",
    "    normDataSet=normDataSet/tile(ranges,(m,1))\n",
    "    return normDataSet, ranges, minVals\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#测试约会网站分类结果代码\n",
    "def datingClassTest():\n",
    "    hoRatio = 0.10      #hold out 10%\n",
    "    datingDataMat,datingLabels = file2matrix('datingTestSet2.txt')       #load data setfrom file\n",
    "    normMat, ranges, minVals = autoNorm(datingDataMat)\n",
    "    m = normMat.shape[0]\n",
    "    numTestVecs = int(m*hoRatio)\n",
    "    errorCount = 0.0\n",
    "    for i in range(numTestVecs):\n",
    "        classifierResult = classify0(normMat[i,:],normMat[numTestVecs:m,:],datingLabels[numTestVecs:m],3)\n",
    "        print(\"the classifier came back with: %d, the real answer is: %d\" % (classifierResult, datingLabels[i]))\n",
    "        if (classifierResult != datingLabels[i]): errorCount += 1.0\n",
    "    print(\"the total error rate is: %f\" % (errorCount/float(numTestVecs)))\n",
    "    #print(errorCount)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#完整的约会网站预测：给定一个人，判断时候适合约会\n",
    "def classifyPerson():\n",
    "    resultList=['not at all','in small doses','in large doses']\n",
    "    percentTats=float(input(\"percentage of time spent playing video games?\"))\n",
    "    #书中raw_input在python3中修改为input（）\n",
    "    ffMiles=float(input(\"frequent flier miles earned per year?\"))\n",
    "    iceCream=float(input(\"liters of ice cream consumed per year?\"))\n",
    "    datingDataMat,datingLabels=file2matrix('datingTestSet2.txt')#原书没有2\n",
    "    normMat, ranges, minVals = autoNorm(datingDataMat)\n",
    "    inArr=array([ffMiles,percentTats,iceCream])\n",
    "    classifierResult=classify0((inArr-minVals)/ranges,normMat,datingLabels,3)\n",
    "    print(\"You will probably like this person:\", resultList[classifierResult-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import os, sys\n",
    "def img2vector(filename):\n",
    "    returnVect=zeros((1,1024))#每个手写识别为32x32大小的二进制图像矩阵 转换为1x1024 numpy向量数组returnVect\n",
    "    fr=open(filename)#打开指定文件\n",
    "    for i in range(32):#循环读出前32行\n",
    "        lineStr=fr.readline()\n",
    "        for j in range(32):\n",
    "            returnVect[0,32*i+j]=int(lineStr[j])#将每行的32个字符值存储在numpy数组中\n",
    "    return returnVect"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#测试算法\n",
    "def handwritingClassTest():\n",
    "    hwLabels=[]\n",
    "    trainingFileList=os.listdir('trainingDigits')#修改 import os 这里加上os.\n",
    "    m=len(trainingFileList)\n",
    "    trainingMat=zeros((m,1024)) #定义文件数x每个向量的训练集\n",
    "    for i in range(m):\n",
    "        fileNameStr=trainingFileList[i]\n",
    "        fileStr=fileNameStr.split('.')[0]#解析文件\n",
    "        classNumStr=int(fileStr.split('_')[0])#解析文件名\n",
    "        hwLabels.append(classNumStr)#存储类别\n",
    "        trainingMat[i,:]=img2vector('trainingDigits/%s'%fileNameStr) #访问第i个文件内的数据\n",
    "    #测试数据集\n",
    "    testFileList=os.listdir('testDigits')\n",
    "    errorCount=0.0\n",
    "    mTest=len(testFileList)\n",
    "    for i in range(mTest):\n",
    "        fileNameStr=testFileList[i]\n",
    "        fileStr=fileNameStr.split('.')[0]\n",
    "        classNumStr=int(fileStr.split('_')[0])#从文件名中分离出数字作为基准\n",
    "        vectorUnderTest=img2vector('testDigits/%s'%fileNameStr)#访问第i个文件内的测试数据，不存储类 直接测试\n",
    "        classifierResult=classify0(vectorUnderTest,trainingMat,hwLabels,3)\n",
    "        print(\"the classifier came back with: %d,the real answer is: %d\" %(classifierResult,classNumStr))\n",
    "        if(classifierResult!=classNumStr):\n",
    "            errorCount+=1.0\n",
    "        print(\"\\nthe total number of errors is: %d\" % errorCount)\n",
    "        print(\"\\nthe total rate is:%f\"% (errorCount/float(mTest)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['A', 'A', 'B', 'B']"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##   \n",
    "group,labels = createDataSet()\n",
    "##   \n",
    "group\n",
    "##   \n",
    "labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'A'"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#P19\n",
    "# def classify0(inX, dataSet, labels, k):\n",
    "#有4个输入参数：用于分类的输入向量是inA\n",
    "#输入的训练样本集为dataSet\n",
    "#标签向量labels\n",
    "#最后的参数k表示用于选择最近邻居的数目\n",
    "# see kNN.py\n",
    "  \n",
    "classify0([0,0], group, labels, 3)\n",
    "classify0([0,0],group,labels,3)\n",
    "classify0([1,0],group,labels,3)\n",
    "classify0([2,2],group,labels,3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#https://www.cnblogs.com/erbaodabao0611/p/7588840.html\n",
    "import matplotlib.lines as mlines\n",
    "def DrawScatter(dataMat,label_list):\n",
    "    # 导入中文字体，及字体大小\n",
    "    #zhfont = FontProperties(fname='C:/Windows/Fonts/simsun.ttc', size=14)\n",
    "    # 绘制绘图窗口 2行2列\n",
    "    fig,ax = plt.subplots(2,2,figsize=(13,8))\n",
    "    # 不同标签赋予不同颜色\n",
    "    label_color = []\n",
    "    for i in label_list:\n",
    "        if i == 1:\n",
    "            label_color.append('black')\n",
    "        elif i == 2:\n",
    "            label_color.append('orange')\n",
    "        elif i == 3:\n",
    "            label_color.append('red')\n",
    "    # 开始绘制散点图 设定散点尺寸与透明度\n",
    "    scatter_size = 12\n",
    "    scatter_alpha = 0.5\n",
    "    # ===================散点图========================\n",
    "    ax[0][0].scatter(dataMat[:,0], dataMat[:,1],color = label_color,s = scatter_size ,alpha = scatter_alpha)\n",
    "    ax[0][1].scatter(dataMat[:, 1], dataMat[:, 2], color=label_color, s=scatter_size , alpha=scatter_alpha)\n",
    "    ax[1][0].scatter(dataMat[:, 0], dataMat[:, 2], color=label_color, s=scatter_size , alpha=scatter_alpha)\n",
    "\n",
    "    # 坐标轴标题\n",
    "    title_list = ['Miles vs Times',\n",
    "                  'Miles vs Kilos',\n",
    "                  'Times vs Kilos']\n",
    "    x_name_list = ['Miles','Times','Kilos']\n",
    "    y_name_list = ['Times','Kilos','Miles']\n",
    "    #设置图例    '''\n",
    "    didntLike = mlines.Line2D([], [], color='black', marker='.',\n",
    "                      markersize=6, label='didntLike')\n",
    "    smallDoses = mlines.Line2D([], [], color='orange', marker='.',\n",
    "                      markersize=6, label='smallDoses')\n",
    "    largeDoses = mlines.Line2D([], [], color='red', marker='.',\n",
    "                      markersize=6, label='largeDoses')\n",
    "    \n",
    "    p = 0\n",
    "    for i in range(2):\n",
    "        for j in range(2):\n",
    "            if p > 2:\n",
    "                break\n",
    "            # 设置坐标轴名称和标题  ,FontProperties = zhfont\n",
    "            plt.setp(ax[i][j].set_title(u'%s'%(title_list[p])),size=9, weight='bold', color='red')\n",
    "            plt.setp(ax[i][j].set_xlabel(u'%s'%(x_name_list[p])), size=7, weight='bold', color='black')\n",
    "            plt.setp(ax[i][j].set_ylabel(u'%s'%(y_name_list[p])), size=7, weight='bold', color='black')\n",
    "            p+=1\n",
    "    # 添加图例\n",
    "    ax[0][0].legend(handles=[didntLike, smallDoses, largeDoses])\n",
    "    ax[0][1].legend(handles=[didntLike, smallDoses, largeDoses])\n",
    "    ax[1][0].legend(handles=[didntLike, smallDoses, largeDoses])\n",
    "\n",
    "    plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[3, 2, 1, 1, 1, 1, 3, 3, 1, 3, 1, 1, 2, 1, 1, 1, 1, 1, 2, 3]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "###例子P21 用K近邻算法改进约会网站的配对效果\n",
    "#约会数据存放在文件datingTestSet2.txt\n",
    "\n",
    "#\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "#%matplotlib inline\n",
    "#import kNN\n",
    "from numpy import * \n",
    "#1确定分类目标和特征\n",
    "# 人划分为三种类：喜欢的;魅力一般;颇具魅力的人 \n",
    "# 包含以下3特征：每年获得的飞行常客里程数；玩视频游戏所耗时间间百分比；每周消赀的冰淇淋公升数 \n",
    "\n",
    "#2、加载数据\n",
    "# def file2matrix(filename):\n",
    "# 用file2matrix函数，处理输入格式问题\n",
    "datingDataMat,datingLabels = file2matrix('datingTestSet2.txt')\n",
    "#   \n",
    "datingDataMat\n",
    "#   \n",
    "datingLabels[0:20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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VCpPJxPjx48k8nUyEt5mYU2XExKTTNagQ7LyEgIhdCD59xHj5J7BUFBN3zoK7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7YiOjq6uVKxw+Fg+PDhnHXWWUycOJHt27c3S4G2R0hICMXFxc2VQW02W4sKpr17925Tmbqp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c/vlPMYU75xyZMPcGNpu4S69bJ5P3uDgZC8j7efNg2jQRcXfgp7B9+3b+8Y9/oGkawcHB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\nAABZ/gddJOniCrvwcAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb23b133be0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#用Matplotlib作图看一下数据信息\n",
    "DrawScatter(datingDataMat,datingLabels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Z2Qr9mPpAA9i7W4YSNsHk+WzFTvYJSrAJ2yoNv7T8lpbcolPeRCG/lX/iLEdm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\nWMQZhmEqmP8PfDD17yKbZMIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb23d6bc630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#3、散点图\n",
    "## 散点图  \n",
    "datingDatas, datingLabels = file2matrix('datingTestSet2.txt')\n",
    "#可视化样本数据显示\n",
    "fig = plt.figure('data_show')\n",
    "ax = fig.add_subplot(111)\n",
    "for i in range(datingDatas.shape[0]):\n",
    "    if datingLabels[i]==1:\n",
    "       ax.scatter(datingDatas[i, 0], datingDatas[i, 1], marker=\"*\",c='r')  # 用后两个特征绘图\n",
    "\n",
    "    if datingLabels[i]==2:\n",
    "       ax.scatter(datingDatas[i, 0], datingDatas[i, 1], marker=\"s\", c='g')  # 用后两个特征绘图\n",
    "\n",
    "    if datingLabels[i]==3:\n",
    "       ax.scatter(datingDatas[i, 0], datingDatas[i, 1], marker=\"^\", c='b')  # 用后两个特征绘图\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#figure创建一个绘图对象\n",
    "fig = plt.figure() \n",
    "ax = fig.add_subplot(111)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"\\n其中，xy是点的坐标，s点的大小\\nmaker是形状可以maker=（5，1）5表示形状是5边型，1表示是星型（0表示多边形，2放射型，3圆形）\\nalpha表示透明度；facecolor=‘none’表示不填充。\\nmatplotlib.pyplot.scatter(x, y, s=20, c='b', marker='o', cmap=None, norm=None, vmin=None, vmax=None, alpha=None, linewidths=None, verts=None, hold=None,**kwargs)\\nax.scatter(datingDataMat[:,1],datingDataMat[:,2],15.0*array(datingLabels),marker=(5,1),alpha=0.5)\\n\""
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 若参数为349，意思是：将画布分割成3行4列，图像画在从左到右从上到下的第9块，\n",
    "\n",
    "'''\n",
    "其中，xy是点的坐标，s点的大小\n",
    "maker是形状可以maker=（5，1）5表示形状是5边型，1表示是星型（0表示多边形，2放射型，3圆形）\n",
    "alpha表示透明度；facecolor=‘none’表示不填充。\n",
    "matplotlib.pyplot.scatter(x, y, s=20, c='b', marker='o', cmap=None, norm=None, vmin=None, vmax=None, alpha=None, linewidths=None, verts=None, hold=None,**kwargs)\n",
    "ax.scatter(datingDataMat[:,1],datingDataMat[:,2],15.0*array(datingLabels),marker=(5,1),alpha=0.5)\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\n#画图\\nax.scatter(datingDataMat[:,1],datingDataMat[:,2])\\nplt.show()\\n'"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''\n",
    "#画图\n",
    "ax.scatter(datingDataMat[:,1],datingDataMat[:,2])\n",
    "plt.show()\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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YekLqEiNaWC8xfOjV1BpjCbCdgajFO+lYuWbcaPZtOsDq39YTFWfniW/vD8o1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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb238a5c208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#画图作色\n",
    "ax.scatter(datingDataMat[:,1], datingDataMat[:,2],15.0*array(datingLabels), 15.0*array(datingLabels))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.      , 0.      , 0.001156])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#4、归一化特征值\n",
    "#把每个特征值的大小固定在[0,1]:\n",
    "#range = MaxVal - MinVal\n",
    "#normVal = rawVal / (MaxVal - MinVal)\n",
    "##reload(kNN)\n",
    "normMat, ranges, minVals = autoNorm(datingDataMat)\n",
    "normMat\n",
    "ranges\n",
    "minVals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 3, the real answer is: 2\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 3, the real answer is: 1\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 3, the real answer is: 1\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 2, the real answer is: 3\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 3, the real answer is: 1\n",
      "the total error rate is: 0.050000\n"
     ]
    }
   ],
   "source": [
    "#5.分类器测试\n",
    "#用10%的数据作为输入来测试，\n",
    "#另外90%作为已知集合\n",
    "datingClassTest()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "percentage of time spent playing video games?10\n",
      "frequent flier miles earned per year?10000\n",
      "liters of ice cream consumed per year?0.6\n",
      "You will probably like this person: in small doses\n"
     ]
    }
   ],
   "source": [
    "#6、约会网站测试\n",
    "#分别输入 10 10000 0.6\n",
    "classifyPerson()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "###例子P29 用K近邻算法手写识别\n",
    "#数据在trainDigit和testDigit里面\n",
    "#程序是kNNOCR.py\n",
    "\n",
    "#1 准备数据：将图像转换为测试向量\n",
    "def img_to_vector(filename):\n",
    "    \"\"\"\n",
    "    将图像转换为向量：该函数创建1×1024的NumPy数组，然后打开给定的文件，\n",
    "    循环读出文件的前32行，并将每行的头32个字符值存储在NumPy数组中，最后返回数组。\n",
    "    \"\"\"\n",
    "    # 创建1×1024的NumPy数组\n",
    "    return_vect = np.zeros((1, 1024))\n",
    "    # 打开给定的文件名\n",
    "    fr = open(filename)\n",
    "    # 循环读出文件的前32行\n",
    "    for i in range(32):\n",
    "        line_str = fr.readline()\n",
    "        # 将每行的头32个字符值存储在NumPy数组\n",
    "        for j in range(32):\n",
    "            return_vect[0, 32 * i + j] = int(line_str[j])\n",
    "    # 返回数组\n",
    "    return return_vect"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 0., ..., 0., 0., 0.]])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#def img2vector(filename):\n",
    "#转化为1*1024的特征向量。文件名如3_3.txt\n",
    "testVector = img2vector('testDigits/0_3.txt')\n",
    "testVector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#测试图像格式化为向量函数效果\n",
    "testVector[0,0:31]\n",
    "testVector[0,32:63]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#将训练集图片合并成100*1024的大矩阵，\n",
    "#同时逐一对测试集中的样本分类\n",
    "\n",
    "#函数classify()为分类主体函数，计算欧式距离\n",
    "#并最终返回测试图片类别："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#测试算法：使用 k-近邻算法识别手写数字\n",
    "def handwriting_class_test():\n",
    "    \"\"\"\n",
    "    测试手写数字识别系统的kNN分类器\n",
    "    \"\"\"\n",
    "    # 创建测试集标签\n",
    "    hw_labels = []\n",
    "    # 加载训练数据\n",
    "    training_file_list = os.listdir('trainingDigits')\n",
    "    # 获取文件夹下文件的个数\n",
    "    m = len(training_file_list)\n",
    "    # 创建一个m行1024列的训练矩阵，该矩阵的每行数据存储一个图像\n",
    "    training_mat = np.zeros((m, 1024))\n",
    "    # 从文件名中解析出分类数字，如文件9_45.txt的分类是9，它是数字9的第45个实例\n",
    "    for i in range(m):\n",
    "        # 获取文件名\n",
    "        file_name_str = training_file_list[i]\n",
    "        # 去掉 .txt\n",
    "        file_str = file_name_str.split('.')[0]\n",
    "        # 获取分类数字\n",
    "        class_num_str = int(file_str.split('_')[0])\n",
    "        # 将获取到的分类数字添加到标签向量中\n",
    "        hw_labels.append(class_num_str)\n",
    "        # 将每一个文件的1x1024数据存储到训练矩阵中\n",
    "        training_mat[i, :] = img_to_vector('trainingDigits/%s' % file_name_str)\n",
    "    # 加载测试数据\n",
    "    test_file_list = os.listdir('testDigits')\n",
    "    # 错误计数\n",
    "    error_count = 0.0\n",
    "    # 测试数据的个数\n",
    "    m_test = len(test_file_list)\n",
    "    # 从测试数据文件名中解析出分类数字\n",
    "    for i in range(m_test):\n",
    "        # 获取文件名\n",
    "        file_name_str = test_file_list[i]\n",
    "        # 去掉 .txt\n",
    "        file_str = file_name_str.split('.')[0]\n",
    "        # 获取分类数字\n",
    "        class_num_str = int(file_str.split('_')[0])\n",
    "        # 获取测试集的1x1024向量,用于训练\n",
    "        vector_under_test = img_to_vector('testDigits/%s' % file_name_str)\n",
    "        # 返回分类结果\n",
    "        classifier_result = classify0(vector_under_test, training_mat, hw_labels, 3)\n",
    "        print(\"the classifier came back with: %d, the real answer is: %d\" % (classifier_result, class_num_str))\n",
    "        if (classifier_result != class_num_str): error_count += 1.0\n",
    "    # 输出错误个数\n",
    "    print(\"\\nthe total number of errors is: %d\" % error_count)\n",
    "    # 输出错误率\n",
    "    print(\"\\nthe total error rate is: %f\" % (error_count / float(m_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the classifier came back with: 4, the real answer is: 4\n",
      "the classifier came back with: 5, the real answer is: 5\n",
      "the classifier came back with: 6, the real answer is: 6\n",
      "the classifier came back with: 5, the real answer is: 5\n",
      "the classifier came back with: 9, the real answer is: 9\n",
      "the classifier came back with: 9, the real answer is: 9\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 5, the real answer is: 5\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 6, the real answer is: 6\n",
      "the classifier came back with: 7, the real answer is: 7\n",
      "the classifier came back with: 6, the real answer is: 6\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 5, the real answer is: 5\n",
      "the classifier came back with: 8, the real answer is: 8\n",
      "the classifier came back with: 5, the real answer is: 5\n",
      "the classifier came back with: 5, the real answer is: 5\n",
      "the classifier came back with: 8, the real answer is: 8\n",
      "the classifier came back with: 5, the real answer is: 5\n",
      "the classifier came back with: 6, the real answer is: 6\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 7, the real answer is: 7\n",
      "the classifier came back with: 6, the real answer is: 6\n",
      "the classifier came back with: 4, the real answer is: 4\n",
      "the classifier came back with: 6, the real answer is: 6\n",
      "the classifier came back with: 7, the real answer is: 7\n",
      "the classifier came back with: 8, the real answer is: 8\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 9, the real answer is: 9\n",
      "the classifier came back with: 4, the real answer is: 4\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 6, the real answer is: 6\n",
      "the classifier came back with: 4, the real answer is: 4\n",
      "the classifier came back with: 5, the real answer is: 5\n",
      "the classifier came back with: 9, the real answer is: 9\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 9, the real answer is: 9\n",
      "the classifier came back with: 7, the real answer is: 7\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 4, the real answer is: 4\n",
      "the classifier came back with: 0, the real answer is: 0\n",
      "the classifier came back with: 7, the real answer is: 7\n",
      "the classifier came back with: 0, the real answer is: 0\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 7, the real answer is: 7\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 4, the real answer is: 4\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 8, the real answer is: 8\n",
      "the classifier came back with: 8, the real answer is: 8\n",
      "the classifier came back with: 9, the real answer is: 9\n",
      "the classifier came back with: 6, the real answer is: 6\n",
      "the classifier came back with: 0, the real answer is: 0\n",
      "the classifier came back with: 9, the real answer is: 9\n",
      "the classifier came back with: 7, the real answer is: 7\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 9, the real answer is: 9\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 8, the real answer is: 8\n",
      "the classifier came back with: 9, the real answer is: 9\n",
      "the classifier came back with: 4, the real answer is: 4\n",
      "the classifier came back with: 5, the real answer is: 5\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 4, the real answer is: 4\n",
      "the classifier came back with: 5, the real answer is: 5\n",
      "the classifier came back with: 0, the real answer is: 0\n",
      "the classifier came back with: 9, the real answer is: 9\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 4, the real answer is: 4\n",
      "the classifier came back with: 5, the real answer is: 5\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 5, the real answer is: 5\n",
      "the classifier came back with: 0, the real answer is: 0\n",
      "the classifier came back with: 8, the real answer is: 8\n",
      "the classifier came back with: 8, the real answer is: 8\n",
      "the classifier came back with: 5, the real answer is: 5\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 0, the real answer is: 0\n",
      "the classifier came back with: 8, the real answer is: 8\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 0, the real answer is: 0\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 9, the real answer is: 9\n",
      "the classifier came back with: 4, the real answer is: 4\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 6, the real answer is: 6\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 8, the real answer is: 8\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 9, the real answer is: 9\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 8, the real answer is: 8\n",
      "the classifier came back with: 9, the real answer is: 9\n",
      "the classifier came back with: 4, the real answer is: 4\n",
      "the classifier came back with: 5, the real answer is: 5\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 0, the real answer is: 0\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 9, the real answer is: 9\n",
      "the classifier came back with: 7, the real answer is: 7\n",
      "the classifier came back with: 9, the real answer is: 9\n",
      "the classifier came back with: 4, the real answer is: 4\n",
      "the classifier came back with: 6, the real answer is: 6\n",
      "the classifier came back with: 9, the real answer is: 9\n",
      "the classifier came back with: 4, the real answer is: 4\n",
      "the classifier came back with: 8, the real answer is: 8\n",
      "the classifier came back with: 8, the real answer is: 8\n",
      "the classifier came back with: 8, the real answer is: 8\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 7, the real answer is: 7\n",
      "the classifier came back with: 6, the real answer is: 6\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 4, the real answer is: 4\n",
      "the classifier came back with: 7, the real answer is: 7\n",
      "the classifier came back with: 8, the real answer is: 8\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 5, the real answer is: 5\n",
      "the classifier came back with: 6, the real answer is: 6\n",
      "the classifier came back with: 6, the real answer is: 6\n",
      "the classifier came back with: 0, the real answer is: 0\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 4, the real answer is: 4\n",
      "the classifier came back with: 6, the real answer is: 6\n",
      "the classifier came back with: 4, the real answer is: 4\n",
      "the classifier came back with: 0, the real answer is: 0\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 4, the real answer is: 4\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 5, the real answer is: 5\n",
      "the classifier came back with: 0, the real answer is: 0\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 6, the real answer is: 6\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 9, the real answer is: 9\n",
      "the classifier came back with: 8, the real answer is: 8\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 6, the real answer is: 6\n",
      "the classifier came back with: 6, the real answer is: 6\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 5, the real answer is: 5\n",
      "the classifier came back with: 3, the real answer is: 8\n",
      "the classifier came back with: 4, the real answer is: 4\n",
      "the classifier came back with: 8, the real answer is: 8\n",
      "the classifier came back with: 0, the real answer is: 0\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 5, the real answer is: 5\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 9, the real answer is: 9\n",
      "the classifier came back with: 5, the real answer is: 5\n",
      "the classifier came back with: 4, the real answer is: 4\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 5, the real answer is: 5\n",
      "the classifier came back with: 7, the real answer is: 7\n",
      "the classifier came back with: 0, the real answer is: 0\n",
      "the classifier came back with: 6, the real answer is: 6\n",
      "the classifier came back with: 5, the real answer is: 5\n",
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      "the classifier came back with: 4, the real answer is: 4\n",
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      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 6, the real answer is: 6\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 9, the real answer is: 9\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 4, the real answer is: 4\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 7, the real answer is: 7\n",
      "the classifier came back with: 0, the real answer is: 0\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "\n",
      "the total number of errors is: 10\n",
      "\n",
      "the total error rate is: 0.010571\n"
     ]
    }
   ],
   "source": [
    "#函数handwritingClassTest做测试\n",
    "handwriting_class_test()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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